OpenRaiser/Intern-Atlas huggingface dataset:
Semantic Granularity Metric Learning for Visual Search
Venue: arXiv cs.CV 2019
Year: 2019

Deep metric learning applied to various applications has shown promising results in identification, retrieval and recognition. Existing methods often do not consider different granularity in visual similarity. However, in many domain applications, images exhibit similarity at multiple granularities with visual semantic concepts, e.g. fashion demonstrates similarity ranging from clothing of the exact same instance to similar looks/design or a common category. Therefore, training image triplets/pairs used for metric learning inherently possess different degree of information. However, the existing methods often treats them with equal importance during training. This hinders capturing the underlying granularities in feature similarity required for effective visual search. In view of this, we propose a new deep semantic granularity metric learning (SGML) that develops a novel idea of leveraging attribute semantic space to capture different granularity of similarity, and then integrate this information into deep metric learning. The proposed method simultaneously learns image attributes and embeddings using multitask CNNs. The two tasks are not only jointly optimized but are further linked by the semantic granularity similarity mappings to leverage the correlations between the tasks. To this end, we propose a new soft-binomial deviance loss that effectively integrates the degree of information in training samples, which helps to capture visual similarity at multiple granularities. Compared to recent ensemble-based methods, our framework is conceptually elegant, computationally simple and provides better performance. We perform extensive experiments on benchmark metric learning datasets and demonstrate that our method outperforms recent state-of-the-art methods, e.g., 1-4.5\% improvement in Recall@1 over the previous state-of-the-arts [1],[2] on DeepFashion In-Shop dataset.
Double Circulant Self-Dual Codes From Generalized Cyclotomic Classes of Order Two
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, constructions of some double circulant self-dual codes by generalized cyclotomic classes of order two are presented. This technique is applied to [72, 36, 12] binary highest know self-dual codes to obtain self-dual codes over GF(2) and [32, 16, 8] almost optimal self-dual codes over GF(4). Based on the properties of generalized cyclotomy, some of these codes can be proved to possess good minimum weights.
Kind Inference for Datatypes: Technical Supplement
Venue: arXiv cs.PL 2019
Year: 2019

In recent years, languages like Haskell have seen a dramatic surge of new features that significantly extends the expressive power of their type systems. With these features, the challenge of kind inference for datatype declarations has presented itself and become a worthy research problem on its own. This paper studies kind inference for datatypes. Inspired by previous research on type-inference, we offer declarative specifications for what datatype declarations should be accepted, both for Haskell98 and for a more advanced system we call PolyKinds, based on the extensions in modern Haskell, including a limited form of dependent types. We believe these formulations to be novel and without precedent, even for Haskell98. These specifications are complemented with implementable algorithmic versions. We study soundness, completeness and the existence of principal kinds in these systems, proving the properties where they hold. This work can serve as a guide both to language designers who wish to formalize their datatype declarations and also to implementors keen to have principled inference of principal types. This technical supplement to Kind Inference for Datatypes serves to expand upon the text in the main paper. It contains detailed typing rules, proofs, and connections to the Glasgow Haskell Compiler (GHC).
Learning Model Bias
Venue: arXiv cs.LG 2019
Year: 2019

In this paper the problem of {\em learning} appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks from the same domain, and a theorem is given bounding the number tasks that must be learnt. A corollary of the theorem is that if the tasks are known to possess a common {\em internal representation} or {\em preprocessing} then the number of examples required per task for good generalisation when learning $n$ tasks simultaneously scales like $O(a + \frac{b}{n})$, where $O(a)$ is a bound on the minimum number of examples required to learn a single task, and $O(a + b)$ is a bound on the number of examples required to learn each task independently. An experiment providing strong qualitative support for the theoretical results is reported.
Towards automatic extractive text summarization of A-133 Single Audit reports with machine learning
Venue: arXiv cs.CL 2019
Year: 2019

The rapid growth of text data has motivated the development of machine-learning based automatic text summarization strategies that concisely capture the essential ideas in a larger text. This study aimed to devise an extractive summarization method for A-133 Single Audits, which assess if recipients of federal grants are compliant with program requirements for use of federal funding. Currently, these voluminous audits must be manually analyzed by officials for oversight, risk management, and prioritization purposes. Automated summarization has the potential to streamline these processes. Analysis focused on the "Findings" section of ~20,000 Single Audits spanning 2016-2018. Following text preprocessing and GloVe embedding, sentence-level k-means clustering was performed to partition sentences by topic and to establish the importance of each sentence. For each audit, key summary sentences were extracted by proximity to cluster centroids. Summaries were judged by non-expert human evaluation and compared to human-generated summaries using the ROUGE metric. Though the goal was to fully automate summarization of A-133 audits, human input was required at various stages due to large variability in audit writing style, content, and context. Examples of human inputs include the number of clusters, the choice to keep or discard certain clusters based on their content relevance, and the definition of a top sentence. Overall, this approach made progress towards automated extractive summaries of A-133 audits, with future work to focus on full automation and improving summary consistency. This work highlights the inherent difficulty and subjective nature of automated summarization in a real-world application.
Harnessing spatial MRI normalization: patch individual filter layers for CNNs
Venue: arXiv cs.CV 2019
Year: 2019

Neuroimaging studies based on magnetic resonance imaging (MRI) typically employ rigorous forms of preprocessing. Images are spatially normalized to a standard template using linear and non-linear transformations. Thus, one can assume that a patch at location (x, y, height, width) contains the same brain region across the entire data set. Most analyses applied on brain MRI using convolutional neural networks (CNNs) ignore this distinction from natural images. Here, we suggest a new layer type called patch individual filter (PIF) layer, which trains higher-level filters locally as we assume that more abstract features are locally specific after spatial normalization. We evaluate PIF layers on three different tasks, namely sex classification as well as either Alzheimer's disease (AD) or multiple sclerosis (MS) detection. We demonstrate that CNNs using PIF layers outperform their counterparts in several, especially low sample size settings.
Detecting Safety and Security Faults in PLC Systems with Data Provenance
Venue: arXiv cs.CR 2019
Year: 2019

Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies.
The Canonical Distortion Measure for Vector Quantization and Function Approximation
Venue: arXiv cs.LG 2019
Year: 2019

To measure the quality of a set of vector quantization points a means of measuring the distance between a random point and its quantization is required. Common metrics such as the {\em Hamming} and {\em Euclidean} metrics, while mathematically simple, are inappropriate for comparing natural signals such as speech or images. In this paper it is shown how an {\em environment} of functions on an input space $X$ induces a {\em canonical distortion measure} (CDM) on X. The depiction 'canonical" is justified because it is shown that optimizing the reconstruction error of X with respect to the CDM gives rise to optimal piecewise constant approximations of the functions in the environment. The CDM is calculated in closed form for several different function classes. An algorithm for training neural networks to implement the CDM is presented along with some encouraging experimental results.
Language Inclusion for Finite Prime Event Structures
Venue: arXiv cs.FL 2019
Year: 2019

We study the problem of language inclusion between finite, labeled prime event structures. Prime event structures are a formalism to compactly represent concurrent behavior of discrete systems. A labeled prime event structure induces a language of sequences of labels produced by the represented system. We study the problem of deciding inclusion and membership for languages encoded by finite prime event structures and provide complexity results for both problems. We provide a family of examples where prime event structures are exponentially more succinct than formalisms that do not take concurrency into account. We provide a decision algorithm for language inclusion that exploits this succinctness. Furthermore, we provide an implementation of the algorithm and an evaluation on a series of benchmarks. Finally, we demonstrate how our results can be applied to mutation-based test case generation.
Reduction Monads and Their Signatures
Venue: arXiv cs.PL 2019
Year: 2019

In this work, we study 'reduction monads', which are essentially the same as monads relative to the free functor from sets into multigraphs. Reduction monads account for two aspects of the lambda calculus: on the one hand, in the monadic viewpoint, the lambda calculus is an object equipped with a well-behaved substitution; on the other hand, in the graphical viewpoint, it is an oriented multigraph whose vertices are terms and whose edges witness the reductions between two terms. We study presentations of reduction monads. To this end, we propose a notion of 'reduction signature'. As usual, such a signature plays the role of a virtual presentation, and specifies arities for generating operations---possibly subject to equations---together with arities for generating reduction rules. For each such signature, we define a category of models; any model is, in particular, a reduction monad. If the initial object of this category of models exists, we call it the 'reduction monad presented (or specified) by the given reduction signature'. Our main result identifies a class of reduction signatures which specify a reduction monad in the above sense. We show in the examples that our approach covers several standard variants of the lambda calculus.
Design Requirements of Generic Hand Exoskeletons and Survey of Hand Exoskeletons for Rehabilitation, Assistive or Haptic Use
Venue: arXiv cs.RO 2019
Year: 2019

Most current hand exoskeletons have been designed specifically for rehabilitation, assistive or haptic applications to simplify the design requirements. Clinical studies on post-stroke rehabilitation have shown that adapting assistive or haptic applications into physical therapy sessions significantly improves the motor learning and treatment process. The recent technology can lead to the creation of generic hand exoskeletons that are application-agnostic. In this paper, our motivation is to create guidelines and best practices for generic exoskeletons by reviewing the literature of current devices. First, we describe each application and briefly explain their design requirements, and then list the design selections to achieve these requirements. Then, we detail each selection by investigating the existing exoskeletons based on their design choices, and by highlighting their impact on application types. With the motivation of creating efficient generic exoskeletons in the future, we finally summarize the best practices in the literature.
Modelling EHR timeseries by restricting feature interaction
Venue: arXiv cs.LG 2019
Year: 2019

Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be significant indicators of patients' clinical states and there might be patterns that are unknown to clinicians but are highly predictive of some outcomes. Many of these values are also missing which makes it difficult to apply existing methods like decision trees. We propose a recurrent neural network model that reduces overfitting to noisy observations by limiting interactions between features. We analyze its performance on mortality, ICD-9 and AKI prediction from observational values on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Our models result in an improvement of 1.1% [p<0.01] in AU-ROC for mortality prediction under the MetaVision subset and 1.0% and 2.2% [p<0.01] respectively for mortality and AKI under the full MIMIC-III dataset compared to existing state-of-the-art interpolation, embedding and decay-based recurrent models.
Weighted Triangle-free 2-matching Problem with Edge-disjoint Forbidden Triangles
Venue: arXiv cs.DS 2019
Year: 2019

The weighted $\mathcal{T}$-free $2$-matching problem is the following problem: given an undirected graph $G$, a weight function on its edge set, and a set $\mathcal{T}$ of triangles in $G$, find a maximum weight $2$-matching containing no triangle in $\mathcal{T}$. When $\mathcal{T}$ is the set of all triangles in $G$, this problem is known as the weighted triangle-free $2$-matching problem, which is a long-standing open problem. A main contribution of this paper is to give a first polynomial-time algorithm for the weighted $\mathcal{T}$-free $2$-matching problem under the assumption that $\mathcal{T}$ is a set of edge-disjoint triangles. In our algorithm, a key ingredient is to give an extended formulation representing the solution set, that is, we introduce new variables and represent the convex hull of the feasible solutions as a projection of another polytope in a higher dimensional space. Although our extended formulation has exponentially many inequalities, we show that the separation problem can be solved in polynomial time, which leads to a polynomial-time algorithm for the weighted $\mathcal{T}$-free $2$-matching problem.
Resource-Competitive Sybil Defenses
Venue: arXiv cs.DC 2019
Year: 2019

Proof-of-work(PoW) is an algorithmic tool used to secure networks by imposing a computational cost on participating devices. Unfortunately, traditional PoW schemes require that correct devices perform significant computational work in perpetuity, even when the system is not under attack. We address this issue by designing general PoW protocols that ensure two properties. First, the fraction of identities in the system that are controlled by an attacker is a minority. Second, the computational cost of our protocol is comparable to the cost of an attacker. In particular, we present an efficient algorithm, GMCOM, which guarantees that the average computational cost to the good ID per unit time is O(J + sqrt(T(J+1))), where J is the average number of joins by the good IDs and T is the average computational spending of the adversary. Additionally, we discuss a precursor to this algorithm, CCOM, which guarantees an average computational cost to good IDs per unit time of O(J+T). We prove a lower bound showing that GMCOM's spending rate is asymptotically optimal among a large family of algorithms. Finally, we provide empirical evidence that our algorithms can be significantly more efficient than previous defenses under various attack scenarios.
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
Venue: arXiv cs.LG 2019
Year: 2019

Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.
Single View Distortion Correction using Semantic Guidance
Venue: arXiv cs.CV 2019
Year: 2019

Most distortion correction methods focus on simple forms of distortion, such as radial or linear distortions. These works undistort images either based on measurements in the presence of a calibration grid, or use multiple views to find point correspondences and predict distortion parameters. When possible distortions are more complex, e.g. in the case of a camera being placed behind a refractive surface such as glass, the standard method is to use a calibration grid. Considering a high variety of distortions, it is nonviable to conduct these measurements. In this work, we present a single view distortion correction method which is capable of undistorting images containing arbitrarily complex distortions by exploiting recent advancements in differentiable image sampling and in the usage of semantic information to augment various tasks. The results of this work show that our model is able to estimate and correct highly complex distortions, and that incorporating semantic information mitigates the process of image undistortion.
A Dynamic Reliability-Aware Service Placement for Network Function Virtualization (NFV)
Venue: arXiv cs.NI 2019
Year: 2019

Network softwarization is one of the major paradigm shifts in the next generation of networks. It enables programmable and flexible management and deployment of the network. Network function virtualization (NFV) is referred to the deployment of software functions running on commodity servers instead of traditional hardware-based middle-boxes. It is an example of network softwarization. In NFV, a service is defined as a chain of software functions named service chain function (SFC). The process of allocating the resources of servers to the services, called service placement, is the most challenging mission in NFV. Dynamic nature of the service arrivals and departures as well as meeting the service level agreement make the service placement problem even more challenging. In this paper, we propose a model for dynamic reliability-aware service placement based on the simultaneous allocation of the main and backup servers. Then, we formulate the dynamic reliability-aware service placement as an infinite horizon Markov decision process (MDP), which aims to minimize the placement cost and maximize the number of admitted services. In the proposed MDP, the number of active services in the network is considered to be the state of the system, and the state of the idle resources is estimated based on it. Also, the number of possible admitted services is considered as the action of the presented MDP. To evaluate each possible action in the proposed MDP, we use a sub-optimal method based on the Viterbi algorithm named Viterbi-based Reliable Static Service Placement (VRSSP) algorithm. We determine the optimal policy based on value iteration method using an algorithm named VRSSP-based Value Iteration (VVI) algorithm. Eventually, through the extensive simulations, the superiority of the proposed model for dynamic reliability-aware service placement compared to the static solutions is inferred.
Position Paper: Towards Transparent Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

Transparent machine learning is introduced as an alternative form of machine learning, where both the model and the learning system are represented in source code form. The goal of this project is to enable direct human understanding of machine learning models, giving us the ability to learn, verify, and refine them as programs. If solved, this technology could represent a best-case scenario for the safety and security of AI systems going forward.
Identification Algorithm to Determine the Trajectory of Robots with Singularities
Venue: arXiv cs.RO 2019
Year: 2019

Singularity in robot controls is an important problem. By identifying an appropriate trajectory for the robots, the singular situations can be avoided. In this paper an identification algorithm is proposed to control the robot such that it can change its direction to avoid the singularity situation. Base on the singular value decomposition, the proposed algorithm is developed for the non-redundant, single-rank robots. The proposed method is employed on a robot with six degrees of freedom, in order to identify its feasible trajectory. Keywords: Singularity; Trajectory identification; Robot control; Identification algorithm; Singular value decomposition.
A Policy Editor for Semantic Sensor Networks
Venue: arXiv cs.AI 2019
Year: 2019

An important use of sensors and actuator networks is to comply with health and safety policies in hazardous environments. In order to deal with increasingly large and dynamic environments, and to quickly react to emergencies, tools are needed to simplify the process of translating high-level policies into executable queries and rules. We present a framework to produce such tools, which uses rules to aggregate low-level sensor data, described using the Semantic Sensor Network Ontology, into more useful and actionable abstractions. Using the schema of the underlying data sources as an input, we automatically generate abstractions which are relevant to the use case at hand. In this demonstration we present a policy editor tool and a simulation on which policies can be tested.